16 citations · 19 across the 3 of their papers we have counts for
5 papers
Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
Eric Wu, Kevin Wu, William Lotter
Data scarcity and class imbalance are two fundamental challenges in many machine learning applications to healthcare. Breast cancer classification in mammography exemplifies these…
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
William Lotter, Abdul Rahman Diab, Bryan Haslam +10
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by…
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Eric Wu, Kevin Wu, David Cox +1
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly availa…
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception
William Lotter, Gabriel Kreiman, David Cox
While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neur…
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
William Lotter, Greg Sorensen, David Cox
Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here,…